
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--data_path", type=str, default=r"D:\gs\code\InDuDoNet\deeplesion\train",
                    help='txt path to training spa-data')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=0)
parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
parser.add_argument('--patchSize', type=int, default=364, help='the height / width of the input image to network')
parser.add_argument('--niter', type=int, default=100, help='total number of training epochs')
parser.add_argument('--batchnum', type=int, default=1000,
                    help='batchsize*batchnum=1000 for randomly selecting 1000 imag pairs at every iteration')
parser.add_argument('--num_channel', type=int, default=32,
                    help='the number of dual channels')  # refer to https://github.com/hongwang01/RCDNet for the channel concatenation strategy
parser.add_argument('--T', type=int, default=4, help='the number of ResBlocks in every ProxNet')
parser.add_argument('--S', type=int, default=10, help='the number of total iterative stages')
parser.add_argument('--resume', type=int, default=1, help='continue to train')
parser.add_argument("--milestone", type=int, default=[2, 3, 4], help="When to decay learning rate")
parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate')
parser.add_argument('--log_dir', default='logs', help='tensorboard logs')
parser.add_argument('--model_dir', default='../models/', help='saving model')
parser.add_argument('--eta1', type=float, default=1, help='initialization for stepsize eta1')
parser.add_argument('--eta2', type=float, default=5, help='initialization for stepsize eta2')
parser.add_argument('--alpha', type=float, default=0.5, help='initialization for weight factor')
parser.add_argument('--gamma', type=float, default=1e-1, help='hyper-parameter for balancing different loss items')
opt = parser.parse_args()